Overview

Dataset statistics

Number of variables21
Number of observations67068
Missing cells442092
Missing cells (%)31.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.7 MiB
Average record size in memory168.0 B

Variable types

Numeric16
DateTime1
Categorical2
Text2

Alerts

carmelo1_post is highly overall correlated with carmelo1_pre and 6 other fieldsHigh correlation
carmelo1_pre is highly overall correlated with carmelo1_post and 6 other fieldsHigh correlation
carmelo2_post is highly overall correlated with carmelo2_pre and 6 other fieldsHigh correlation
carmelo2_pre is highly overall correlated with carmelo2_post and 6 other fieldsHigh correlation
carmelo_prob1 is highly overall correlated with carmelo1_post and 10 other fieldsHigh correlation
carmelo_prob2 is highly overall correlated with carmelo1_post and 10 other fieldsHigh correlation
df_index is highly overall correlated with seasonHigh correlation
elo1_post is highly overall correlated with carmelo1_post and 6 other fieldsHigh correlation
elo1_pre is highly overall correlated with carmelo1_post and 6 other fieldsHigh correlation
elo2_post is highly overall correlated with carmelo2_post and 6 other fieldsHigh correlation
elo2_pre is highly overall correlated with carmelo2_post and 6 other fieldsHigh correlation
elo_prob1 is highly overall correlated with carmelo1_post and 10 other fieldsHigh correlation
elo_prob2 is highly overall correlated with carmelo1_post and 10 other fieldsHigh correlation
neutral is highly overall correlated with playoffHigh correlation
playoff is highly overall correlated with neutralHigh correlation
score1 is highly overall correlated with score2High correlation
score2 is highly overall correlated with score1High correlation
season is highly overall correlated with df_indexHigh correlation
neutral is highly imbalanced (99.5%)Imbalance
playoff is highly imbalanced (84.1%)Imbalance
playoff has 62814 (93.7%) missing valuesMissing
carmelo1_pre has 63157 (94.2%) missing valuesMissing
carmelo2_pre has 63157 (94.2%) missing valuesMissing
carmelo1_post has 63213 (94.3%) missing valuesMissing
carmelo2_post has 63213 (94.3%) missing valuesMissing
carmelo_prob1 has 63157 (94.2%) missing valuesMissing
carmelo_prob2 has 63157 (94.2%) missing valuesMissing
df_index is uniformly distributedUniform
df_index has unique valuesUnique

Reproduction

Analysis started2025-04-05 19:44:33.569120
Analysis finished2025-04-05 19:45:05.021868
Duration31.45 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

df_index
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct67068
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33533.5
Minimum0
Maximum67067
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size524.1 KiB
2025-04-05T20:45:05.073142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3353.35
Q116766.75
median33533.5
Q350300.25
95-th percentile63713.65
Maximum67067
Range67067
Interquartile range (IQR)33533.5

Descriptive statistics

Standard deviation19361.008
Coefficient of variation (CV)0.57736318
Kurtosis-1.2
Mean33533.5
Median Absolute Deviation (MAD)16767
Skewness0
Sum2.2490248 × 109
Variance3.7484864 × 108
MonotonicityStrictly increasing
2025-04-05T20:45:05.163869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
44702 1
 
< 0.1%
44704 1
 
< 0.1%
44705 1
 
< 0.1%
44706 1
 
< 0.1%
44707 1
 
< 0.1%
44708 1
 
< 0.1%
44709 1
 
< 0.1%
44710 1
 
< 0.1%
44711 1
 
< 0.1%
Other values (67058) 67058
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
67067 1
< 0.1%
67066 1
< 0.1%
67065 1
< 0.1%
67064 1
< 0.1%
67063 1
< 0.1%
67062 1
< 0.1%
67061 1
< 0.1%
67060 1
< 0.1%
67059 1
< 0.1%
67058 1
< 0.1%

date
Date

Distinct13023
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size524.1 KiB
Minimum1946-11-01 00:00:00
Maximum2018-04-28 00:00:00
2025-04-05T20:45:05.257231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:45:05.349740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

season
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1989.8793
Minimum1947
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.1 KiB
2025-04-05T20:45:05.433917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1947
5-th percentile1956
Q11975
median1992
Q32006
95-th percentile2016
Maximum2018
Range71
Interquartile range (IQR)31

Descriptive statistics

Standard deviation18.348679
Coefficient of variation (CV)0.009221001
Kurtosis-0.78937266
Mean1989.8793
Median Absolute Deviation (MAD)15
Skewness-0.3581011
Sum1.3345723 × 108
Variance336.67404
MonotonicityIncreasing
2025-04-05T20:45:05.523406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 1319
 
2.0%
2006 1319
 
2.0%
2016 1316
 
2.0%
2008 1316
 
2.0%
2009 1315
 
2.0%
2005 1314
 
2.0%
2013 1314
 
2.0%
2010 1312
 
2.0%
2015 1311
 
2.0%
2011 1311
 
2.0%
Other values (62) 53921
80.4%
ValueCountFrequency (%)
1947 350
0.5%
1948 215
 
0.3%
1949 380
0.6%
1950 593
0.9%
1951 380
0.6%
1952 355
0.5%
1953 374
0.6%
1954 347
0.5%
1955 309
0.5%
1956 311
0.5%
ValueCountFrequency (%)
2018 1286
1.9%
2017 1309
2.0%
2016 1316
2.0%
2015 1311
2.0%
2014 1319
2.0%
2013 1314
2.0%
2012 1074
1.6%
2011 1311
2.0%
2010 1312
2.0%
2009 1315
2.0%

neutral
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size524.1 KiB
0
67041 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters67068
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 67041
> 99.9%
1 27
 
< 0.1%

Length

2025-04-05T20:45:05.603182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-05T20:45:05.669672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 67041
> 99.9%
1 27
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 67041
> 99.9%
1 27
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67068
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 67041
> 99.9%
1 27
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 67068
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 67041
> 99.9%
1 27
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 67041
> 99.9%
1 27
 
< 0.1%

playoff
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)0.1%
Missing62814
Missing (%)93.7%
Memory size524.1 KiB
t
4033 
q
 
144
s
 
43
c
 
22
f
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4254
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt
2nd rowt
3rd rowt
4th rowt
5th rowt

Common Values

ValueCountFrequency (%)
t 4033
 
6.0%
q 144
 
0.2%
s 43
 
0.1%
c 22
 
< 0.1%
f 12
 
< 0.1%
(Missing) 62814
93.7%

Length

2025-04-05T20:45:05.723804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-05T20:45:05.801092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
t 4033
94.8%
q 144
 
3.4%
s 43
 
1.0%
c 22
 
0.5%
f 12
 
0.3%

Most occurring characters

ValueCountFrequency (%)
t 4033
94.8%
q 144
 
3.4%
s 43
 
1.0%
c 22
 
0.5%
f 12
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4254
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 4033
94.8%
q 144
 
3.4%
s 43
 
1.0%
c 22
 
0.5%
f 12
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 4254
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 4033
94.8%
q 144
 
3.4%
s 43
 
1.0%
c 22
 
0.5%
f 12
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4254
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 4033
94.8%
q 144
 
3.4%
s 43
 
1.0%
c 22
 
0.5%
f 12
 
0.3%

team1
Text

Distinct102
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size524.1 KiB
2025-04-05T20:45:06.014613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters201204
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRH
2nd rowCHS
3rd rowPRO
4th rowSTB
5th rowDTF
ValueCountFrequency (%)
bos 3240
 
4.8%
nyk 2979
 
4.4%
lal 2682
 
4.0%
det 2657
 
4.0%
phi 2342
 
3.5%
chi 2307
 
3.4%
atl 2162
 
3.2%
pho 2159
 
3.2%
mil 2133
 
3.2%
por 2066
 
3.1%
Other values (92) 42341
63.1%
2025-04-05T20:45:06.361478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 19493
 
9.7%
L 18368
 
9.1%
S 17131
 
8.5%
N 15221
 
7.6%
O 14563
 
7.2%
I 12747
 
6.3%
H 11603
 
5.8%
C 11475
 
5.7%
E 9476
 
4.7%
T 9208
 
4.6%
Other values (15) 61919
30.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 201204
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 19493
 
9.7%
L 18368
 
9.1%
S 17131
 
8.5%
N 15221
 
7.6%
O 14563
 
7.2%
I 12747
 
6.3%
H 11603
 
5.8%
C 11475
 
5.7%
E 9476
 
4.7%
T 9208
 
4.6%
Other values (15) 61919
30.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 201204
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 19493
 
9.7%
L 18368
 
9.1%
S 17131
 
8.5%
N 15221
 
7.6%
O 14563
 
7.2%
I 12747
 
6.3%
H 11603
 
5.8%
C 11475
 
5.7%
E 9476
 
4.7%
T 9208
 
4.6%
Other values (15) 61919
30.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 201204
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 19493
 
9.7%
L 18368
 
9.1%
S 17131
 
8.5%
N 15221
 
7.6%
O 14563
 
7.2%
I 12747
 
6.3%
H 11603
 
5.8%
C 11475
 
5.7%
E 9476
 
4.7%
T 9208
 
4.6%
Other values (15) 61919
30.8%

team2
Text

Distinct102
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size524.1 KiB
2025-04-05T20:45:06.544514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters201204
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNYK
2nd rowNYK
3rd rowBOS
4th rowPIT
5th rowWSC
ValueCountFrequency (%)
nyk 3036
 
4.5%
bos 3034
 
4.5%
lal 2642
 
3.9%
det 2578
 
3.8%
phi 2444
 
3.6%
chi 2252
 
3.4%
pho 2167
 
3.2%
mil 2160
 
3.2%
atl 2135
 
3.2%
por 2072
 
3.1%
Other values (92) 42548
63.4%
2025-04-05T20:45:06.784680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 19342
 
9.6%
L 18223
 
9.1%
S 17370
 
8.6%
N 15183
 
7.5%
O 14420
 
7.2%
I 12707
 
6.3%
H 11796
 
5.9%
C 11343
 
5.6%
E 9419
 
4.7%
T 9163
 
4.6%
Other values (15) 62238
30.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 201204
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 19342
 
9.6%
L 18223
 
9.1%
S 17370
 
8.6%
N 15183
 
7.5%
O 14420
 
7.2%
I 12707
 
6.3%
H 11796
 
5.9%
C 11343
 
5.6%
E 9419
 
4.7%
T 9163
 
4.6%
Other values (15) 62238
30.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 201204
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 19342
 
9.6%
L 18223
 
9.1%
S 17370
 
8.6%
N 15183
 
7.5%
O 14420
 
7.2%
I 12707
 
6.3%
H 11796
 
5.9%
C 11343
 
5.6%
E 9419
 
4.7%
T 9163
 
4.6%
Other values (15) 62238
30.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 201204
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 19342
 
9.6%
L 18223
 
9.1%
S 17370
 
8.6%
N 15183
 
7.5%
O 14420
 
7.2%
I 12707
 
6.3%
H 11796
 
5.9%
C 11343
 
5.6%
E 9419
 
4.7%
T 9163
 
4.6%
Other values (15) 62238
30.9%

elo1_pre
Real number (ℝ)

HIGH CORRELATION 

Distinct66377
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1496.0651
Minimum1105.6178
Maximum1855.7791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.1 KiB
2025-04-05T20:45:06.890703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1105.6178
5-th percentile1302.3409
Q11418.3397
median1501.3102
Q31576.4048
95-th percentile1674.214
Maximum1855.7791
Range750.16132
Interquartile range (IQR)158.06507

Descriptive statistics

Standard deviation112.50918
Coefficient of variation (CV)0.075203397
Kurtosis-0.32198221
Mean1496.0651
Median Absolute Deviation (MAD)78.62225
Skewness-0.163926
Sum1.003381 × 108
Variance12658.315
MonotonicityNot monotonic
2025-04-05T20:45:06.978395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 27
 
< 0.1%
1732.213741 4
 
< 0.1%
1577.964814 4
 
< 0.1%
1675.616215 4
 
< 0.1%
1660.410398 4
 
< 0.1%
1614.263041 4
 
< 0.1%
1575.480725 4
 
< 0.1%
1552.252717 4
 
< 0.1%
1619.559364 4
 
< 0.1%
1505.736767 3
 
< 0.1%
Other values (66367) 67006
99.9%
ValueCountFrequency (%)
1105.6178 1
< 0.1%
1111.1008 1
< 0.1%
1114.233 1
< 0.1%
1115.0044 1
< 0.1%
1121.7024 1
< 0.1%
1122.8372 1
< 0.1%
1124.9786 1
< 0.1%
1125.157 1
< 0.1%
1126.8462 1
< 0.1%
1127.3315 1
< 0.1%
ValueCountFrequency (%)
1855.779115 1
< 0.1%
1850.460645 1
< 0.1%
1842.598042 1
< 0.1%
1836.6647 1
< 0.1%
1835.723115 1
< 0.1%
1833.25517 1
< 0.1%
1831.6427 1
< 0.1%
1823.389722 1
< 0.1%
1822.217665 1
< 0.1%
1821.749675 1
< 0.1%

elo2_pre
Real number (ℝ)

HIGH CORRELATION 

Distinct66413
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1496.3106
Minimum1091.6445
Maximum1865.4491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.1 KiB
2025-04-05T20:45:07.070835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1091.6445
5-th percentile1304.3333
Q11418.435
median1502.3989
Q31575.8737
95-th percentile1673.2177
Maximum1865.4491
Range773.80457
Interquartile range (IQR)157.43875

Descriptive statistics

Standard deviation111.94622
Coefficient of variation (CV)0.074814831
Kurtosis-0.30963585
Mean1496.3106
Median Absolute Deviation (MAD)78.2479
Skewness-0.16420509
Sum1.0035456 × 108
Variance12531.957
MonotonicityNot monotonic
2025-04-05T20:45:07.164157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 15
 
< 0.1%
1551.421614 4
 
< 0.1%
1482.671174 4
 
< 0.1%
1505.736767 4
 
< 0.1%
1588.290373 4
 
< 0.1%
1514.049444 4
 
< 0.1%
1556.721843 4
 
< 0.1%
1686.339186 4
 
< 0.1%
1580.171433 4
 
< 0.1%
1453.8972 3
 
< 0.1%
Other values (66403) 67018
99.9%
ValueCountFrequency (%)
1091.6445 1
< 0.1%
1094.5652 1
< 0.1%
1100.2919 1
< 0.1%
1118.1117 1
< 0.1%
1118.5309 1
< 0.1%
1121.4919 1
< 0.1%
1124.1189 1
< 0.1%
1124.2645 1
< 0.1%
1124.8531 1
< 0.1%
1127.4946 1
< 0.1%
ValueCountFrequency (%)
1865.449075 1
< 0.1%
1860.313175 1
< 0.1%
1853.1045 1
< 0.1%
1840.988445 1
< 0.1%
1838.7209 1
< 0.1%
1838.617414 1
< 0.1%
1838.361238 1
< 0.1%
1837.243839 1
< 0.1%
1835.143547 1
< 0.1%
1831.72693 1
< 0.1%

elo_prob1
Real number (ℝ)

HIGH CORRELATION 

Distinct66778
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62114763
Minimum0.062615894
Maximum0.98168456
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.1 KiB
2025-04-05T20:45:07.269433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.062615894
5-th percentile0.29761854
Q10.49866184
median0.64004471
Q30.75965576
95-th percentile0.88206697
Maximum0.98168456
Range0.91906867
Interquartile range (IQR)0.26099392

Descriptive statistics

Standard deviation0.1783046
Coefficient of variation (CV)0.28705671
Kurtosis-0.49639395
Mean0.62114763
Median Absolute Deviation (MAD)0.12894957
Skewness-0.40897277
Sum41659.13
Variance0.03179253
MonotonicityNot monotonic
2025-04-05T20:45:07.358839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6400649998 8
 
< 0.1%
0.6371334397 4
 
< 0.1%
0.8342938192 4
 
< 0.1%
0.5476625191 4
 
< 0.1%
0.6341170107 4
 
< 0.1%
0.7169278011 4
 
< 0.1%
0.6737416047 4
 
< 0.1%
0.8124515804 4
 
< 0.1%
0.8437405417 4
 
< 0.1%
0.3857821424 3
 
< 0.1%
Other values (66768) 67025
99.9%
ValueCountFrequency (%)
0.06261589398 1
< 0.1%
0.06485756683 1
< 0.1%
0.06951862661 1
< 0.1%
0.07346634408 1
< 0.1%
0.08217265868 1
< 0.1%
0.08242819854 1
< 0.1%
0.08542119318 1
< 0.1%
0.08697211686 1
< 0.1%
0.08843188379 1
< 0.1%
0.08978249753 1
< 0.1%
ValueCountFrequency (%)
0.981684562 1
< 0.1%
0.9795531802 1
< 0.1%
0.9789025146 1
< 0.1%
0.9768296703 1
< 0.1%
0.9762209969 1
< 0.1%
0.9760993467 1
< 0.1%
0.9757293521 1
< 0.1%
0.9755650759 1
< 0.1%
0.973823123 1
< 0.1%
0.9735862317 1
< 0.1%

elo_prob2
Real number (ℝ)

HIGH CORRELATION 

Distinct66778
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.37885237
Minimum0.018315438
Maximum0.93738411
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.1 KiB
2025-04-05T20:45:07.444595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.018315438
5-th percentile0.11793303
Q10.24034424
median0.35995529
Q30.50133816
95-th percentile0.70238146
Maximum0.93738411
Range0.91906867
Interquartile range (IQR)0.26099392

Descriptive statistics

Standard deviation0.1783046
Coefficient of variation (CV)0.47064402
Kurtosis-0.49639395
Mean0.37885237
Median Absolute Deviation (MAD)0.12894957
Skewness0.40897277
Sum25408.87
Variance0.03179253
MonotonicityNot monotonic
2025-04-05T20:45:07.530445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3599350002 8
 
< 0.1%
0.3628665603 4
 
< 0.1%
0.1657061808 4
 
< 0.1%
0.4523374809 4
 
< 0.1%
0.3658829893 4
 
< 0.1%
0.2830721989 4
 
< 0.1%
0.3262583953 4
 
< 0.1%
0.1875484196 4
 
< 0.1%
0.1562594583 4
 
< 0.1%
0.6142178576 3
 
< 0.1%
Other values (66768) 67025
99.9%
ValueCountFrequency (%)
0.01831543798 1
< 0.1%
0.02044681977 1
< 0.1%
0.02109748536 1
< 0.1%
0.02317032975 1
< 0.1%
0.02377900315 1
< 0.1%
0.0239006533 1
< 0.1%
0.02427064789 1
< 0.1%
0.02443492412 1
< 0.1%
0.02617687701 1
< 0.1%
0.02641376828 1
< 0.1%
ValueCountFrequency (%)
0.937384106 1
< 0.1%
0.9351424332 1
< 0.1%
0.9304813734 1
< 0.1%
0.9265336559 1
< 0.1%
0.9178273413 1
< 0.1%
0.9175718015 1
< 0.1%
0.9145788068 1
< 0.1%
0.9130278831 1
< 0.1%
0.9115681162 1
< 0.1%
0.9102175025 1
< 0.1%

elo1_post
Real number (ℝ)

HIGH CORRELATION 

Distinct66406
Distinct (%)99.1%
Missing56
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1495.9402
Minimum1100.2919
Maximum1860.3132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.1 KiB
2025-04-05T20:45:07.622895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1100.2919
5-th percentile1301.9694
Q11417.6422
median1501.289
Q31576.4615
95-th percentile1674.3334
Maximum1860.3132
Range760.02127
Interquartile range (IQR)158.81935

Descriptive statistics

Standard deviation112.95758
Coefficient of variation (CV)0.075509424
Kurtosis-0.32113184
Mean1495.9402
Median Absolute Deviation (MAD)79.064099
Skewness-0.166129
Sum1.0024594 × 108
Variance12759.415
MonotonicityNot monotonic
2025-04-05T20:45:07.731912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1460.6031 3
 
< 0.1%
1504.1068 3
 
< 0.1%
1455.4626 3
 
< 0.1%
1478.821 2
 
< 0.1%
1483.0547 2
 
< 0.1%
1532.6823 2
 
< 0.1%
1503.1405 2
 
< 0.1%
1497.2789 2
 
< 0.1%
1513.6747 2
 
< 0.1%
1405.4454 2
 
< 0.1%
Other values (66396) 66989
99.9%
(Missing) 56
 
0.1%
ValueCountFrequency (%)
1100.2919 1
< 0.1%
1105.6178 1
< 0.1%
1111.1008 1
< 0.1%
1111.4615 1
< 0.1%
1115.0044 1
< 0.1%
1118.5309 1
< 0.1%
1124.1189 1
< 0.1%
1124.8531 1
< 0.1%
1124.9786 1
< 0.1%
1125.157 1
< 0.1%
ValueCountFrequency (%)
1860.313175 1
< 0.1%
1855.779115 1
< 0.1%
1845.812884 1
< 0.1%
1838.7209 1
< 0.1%
1837.243839 1
< 0.1%
1836.6647 1
< 0.1%
1835.723115 1
< 0.1%
1831.72693 1
< 0.1%
1824.794606 1
< 0.1%
1823.389722 1
< 0.1%

elo2_post
Real number (ℝ)

HIGH CORRELATION 

Distinct66449
Distinct (%)99.2%
Missing56
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1496.2752
Minimum1085.7744
Maximum1865.4491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.1 KiB
2025-04-05T20:45:07.820017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1085.7744
5-th percentile1303.6748
Q11418.4906
median1502.2026
Q31576.0499
95-th percentile1673.5138
Maximum1865.4491
Range779.67467
Interquartile range (IQR)157.5593

Descriptive statistics

Standard deviation112.131
Coefficient of variation (CV)0.074940088
Kurtosis-0.30871978
Mean1496.2752
Median Absolute Deviation (MAD)78.38745
Skewness-0.16289842
Sum1.002684 × 108
Variance12573.361
MonotonicityNot monotonic
2025-04-05T20:45:07.907054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1572.1902 3
 
< 0.1%
1387.0801 3
 
< 0.1%
1553.9567 3
 
< 0.1%
1435.0557 2
 
< 0.1%
1504.3225 2
 
< 0.1%
1521.238 2
 
< 0.1%
1366.3833 2
 
< 0.1%
1465.9939 2
 
< 0.1%
1552.4282 2
 
< 0.1%
1425.8354 2
 
< 0.1%
Other values (66439) 66989
99.9%
(Missing) 56
 
0.1%
ValueCountFrequency (%)
1085.7744 1
< 0.1%
1091.6445 1
< 0.1%
1094.5652 1
< 0.1%
1114.233 1
< 0.1%
1115.2101 1
< 0.1%
1118.1117 1
< 0.1%
1121.4919 1
< 0.1%
1121.7024 1
< 0.1%
1122.8372 1
< 0.1%
1124.2645 1
< 0.1%
ValueCountFrequency (%)
1865.449075 1
< 0.1%
1853.1045 1
< 0.1%
1850.460645 1
< 0.1%
1842.598042 1
< 0.1%
1840.988445 1
< 0.1%
1838.617414 1
< 0.1%
1838.361238 1
< 0.1%
1835.143547 1
< 0.1%
1833.25517 1
< 0.1%
1831.6427 1
< 0.1%

carmelo1_pre
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3869
Distinct (%)98.9%
Missing63157
Missing (%)94.2%
Infinite0
Infinite (%)0.0%
Mean1513.1019
Minimum1187.7308
Maximum1872.6906
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.1 KiB
2025-04-05T20:45:07.998007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1187.7308
5-th percentile1316.9032
Q11435.0651
median1510.7951
Q31584.6358
95-th percentile1733.0481
Maximum1872.6906
Range684.95978
Interquartile range (IQR)149.57073

Descriptive statistics

Standard deviation119.11911
Coefficient of variation (CV)0.078725104
Kurtosis0.033864929
Mean1513.1019
Median Absolute Deviation (MAD)74.436208
Skewness0.20642788
Sum5917741.6
Variance14189.361
MonotonicityNot monotonic
2025-04-05T20:45:08.082026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1585.126925 4
 
< 0.1%
1748.389768 4
 
< 0.1%
1632.277046 4
 
< 0.1%
1551.305868 4
 
< 0.1%
1578.317876 4
 
< 0.1%
1618.401111 4
 
< 0.1%
1670.68453 4
 
< 0.1%
1677.021761 4
 
< 0.1%
1564.62062 3
 
< 0.1%
1507.976699 3
 
< 0.1%
Other values (3859) 3873
 
5.8%
(Missing) 63157
94.2%
ValueCountFrequency (%)
1187.730784 1
< 0.1%
1192.004524 1
< 0.1%
1196.256418 1
< 0.1%
1202.668956 1
< 0.1%
1203.241148 1
< 0.1%
1203.274435 1
< 0.1%
1204.227846 1
< 0.1%
1207.716089 1
< 0.1%
1209.263639 1
< 0.1%
1211.665091 1
< 0.1%
ValueCountFrequency (%)
1872.690559 1
< 0.1%
1867.938354 1
< 0.1%
1857.378738 1
< 0.1%
1842.997775 1
< 0.1%
1841.520859 1
< 0.1%
1839.081442 1
< 0.1%
1836.019944 1
< 0.1%
1829 1
< 0.1%
1825.187764 1
< 0.1%
1824.571864 1
< 0.1%

carmelo2_pre
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3869
Distinct (%)98.9%
Missing63157
Missing (%)94.2%
Infinite0
Infinite (%)0.0%
Mean1510.8348
Minimum1192.8422
Maximum1881.6196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.1 KiB
2025-04-05T20:45:08.170783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1192.8422
5-th percentile1316.5909
Q11433.774
median1508.874
Q31581.3755
95-th percentile1719.9801
Maximum1881.6196
Range688.77744
Interquartile range (IQR)147.60148

Descriptive statistics

Standard deviation118.80537
Coefficient of variation (CV)0.078635575
Kurtosis0.11756218
Mean1510.8348
Median Absolute Deviation (MAD)73.459183
Skewness0.20676723
Sum5908875
Variance14114.715
MonotonicityNot monotonic
2025-04-05T20:45:08.257445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1581.581732 4
 
< 0.1%
1564.62062 4
 
< 0.1%
1677.637894 4
 
< 0.1%
1551.412437 4
 
< 0.1%
1507.976699 4
 
< 0.1%
1599.536602 4
 
< 0.1%
1500.681024 4
 
< 0.1%
1487.322624 4
 
< 0.1%
1748.389768 3
 
< 0.1%
1578.317876 3
 
< 0.1%
Other values (3859) 3873
 
5.8%
(Missing) 63157
94.2%
ValueCountFrequency (%)
1192.842155 1
< 0.1%
1196.354502 1
< 0.1%
1196.451749 1
< 0.1%
1197.007987 1
< 0.1%
1197.669168 1
< 0.1%
1198.609638 1
< 0.1%
1198.99393 1
< 0.1%
1199.375722 1
< 0.1%
1200.538856 1
< 0.1%
1203.960308 1
< 0.1%
ValueCountFrequency (%)
1881.619599 1
< 0.1%
1876.930941 1
< 0.1%
1859.243885 1
< 0.1%
1850.77776 1
< 0.1%
1841.990258 1
< 0.1%
1840.550968 1
< 0.1%
1836.233807 1
< 0.1%
1835.848032 1
< 0.1%
1832.807925 1
< 0.1%
1832.311646 1
< 0.1%

carmelo1_post
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3855
Distinct (%)100.0%
Missing63213
Missing (%)94.3%
Infinite0
Infinite (%)0.0%
Mean1510.7153
Minimum1187.7308
Maximum1876.9309
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.1 KiB
2025-04-05T20:45:08.343392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1187.7308
5-th percentile1314.1497
Q11432.489
median1508.011
Q31582.574
95-th percentile1728.3426
Maximum1876.9309
Range689.20016
Interquartile range (IQR)150.08496

Descriptive statistics

Standard deviation119.63681
Coefficient of variation (CV)0.079192163
Kurtosis0.061388206
Mean1510.7153
Median Absolute Deviation (MAD)75.174295
Skewness0.22673799
Sum5823807.3
Variance14312.966
MonotonicityNot monotonic
2025-04-05T20:45:08.427878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1371.153364 1
 
< 0.1%
1504.544155 1
 
< 0.1%
1539.058424 1
 
< 0.1%
1485.929987 1
 
< 0.1%
1533.227288 1
 
< 0.1%
1527.417228 1
 
< 0.1%
1601.057492 1
 
< 0.1%
1525.063457 1
 
< 0.1%
1479.166326 1
 
< 0.1%
1492.136914 1
 
< 0.1%
Other values (3845) 3845
 
5.7%
(Missing) 63213
94.3%
ValueCountFrequency (%)
1187.730784 1
< 0.1%
1192.842155 1
< 0.1%
1198.609638 1
< 0.1%
1198.99393 1
< 0.1%
1200.538856 1
< 0.1%
1202.668956 1
< 0.1%
1203.241148 1
< 0.1%
1203.960308 1
< 0.1%
1204.227846 1
< 0.1%
1209.263639 1
< 0.1%
ValueCountFrequency (%)
1876.930941 1
< 0.1%
1872.690559 1
< 0.1%
1860.458986 1
< 0.1%
1850.77776 1
< 0.1%
1842.997775 1
< 0.1%
1840.550968 1
< 0.1%
1839.081442 1
< 0.1%
1827.649361 1
< 0.1%
1827.087921 1
< 0.1%
1825.187764 1
< 0.1%

carmelo2_post
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3855
Distinct (%)100.0%
Missing63213
Missing (%)94.3%
Infinite0
Infinite (%)0.0%
Mean1510.7869
Minimum1192.0045
Maximum1881.6196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.1 KiB
2025-04-05T20:45:08.513783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1192.0045
5-th percentile1315.7726
Q11433.3877
median1509.3764
Q31580.5948
95-th percentile1722.2005
Maximum1881.6196
Range689.61507
Interquartile range (IQR)147.20708

Descriptive statistics

Standard deviation118.97948
Coefficient of variation (CV)0.078753323
Kurtosis0.11374746
Mean1510.7869
Median Absolute Deviation (MAD)73.656709
Skewness0.2143962
Sum5824083.3
Variance14156.118
MonotonicityNot monotonic
2025-04-05T20:45:08.598513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1478.367767 1
 
< 0.1%
1557.206989 1
 
< 0.1%
1566.333724 1
 
< 0.1%
1653.96647 1
 
< 0.1%
1578.692081 1
 
< 0.1%
1615.014123 1
 
< 0.1%
1648.659136 1
 
< 0.1%
1580.328691 1
 
< 0.1%
1573.828714 1
 
< 0.1%
1647.759542 1
 
< 0.1%
Other values (3845) 3845
 
5.7%
(Missing) 63213
94.3%
ValueCountFrequency (%)
1192.004524 1
< 0.1%
1196.256418 1
< 0.1%
1196.354502 1
< 0.1%
1196.451749 1
< 0.1%
1197.007987 1
< 0.1%
1197.669168 1
< 0.1%
1197.92597 1
< 0.1%
1199.375722 1
< 0.1%
1203.274435 1
< 0.1%
1207.716089 1
< 0.1%
ValueCountFrequency (%)
1881.619599 1
< 0.1%
1867.938354 1
< 0.1%
1859.243885 1
< 0.1%
1857.378738 1
< 0.1%
1841.990258 1
< 0.1%
1841.520859 1
< 0.1%
1836.233807 1
< 0.1%
1836.019944 1
< 0.1%
1835.848032 1
< 0.1%
1832.807925 1
< 0.1%

carmelo_prob1
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3879
Distinct (%)99.2%
Missing63157
Missing (%)94.2%
Infinite0
Infinite (%)0.0%
Mean0.62754574
Minimum0.060473559
Maximum0.98659284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.1 KiB
2025-04-05T20:45:08.684872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.060473559
5-th percentile0.27589052
Q10.49563599
median0.65312911
Q30.78022763
95-th percentile0.90339718
Maximum0.98659284
Range0.92611928
Interquartile range (IQR)0.28459164

Descriptive statistics

Standard deviation0.19296691
Coefficient of variation (CV)0.30749457
Kurtosis-0.52774075
Mean0.62754574
Median Absolute Deviation (MAD)0.14323585
Skewness-0.44976919
Sum2454.3314
Variance0.037236228
MonotonicityNot monotonic
2025-04-05T20:45:08.775374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8173157516 3
 
< 0.1%
0.3514353297 3
 
< 0.1%
0.5279699503 3
 
< 0.1%
0.3558947059 3
 
< 0.1%
0.5299546374 3
 
< 0.1%
0.6280082386 3
 
< 0.1%
0.6062527719 3
 
< 0.1%
0.3927939686 3
 
< 0.1%
0.8481655679 3
 
< 0.1%
0.6372861707 3
 
< 0.1%
Other values (3869) 3881
 
5.8%
(Missing) 63157
94.2%
ValueCountFrequency (%)
0.06047355853 1
< 0.1%
0.06998686811 1
< 0.1%
0.07763699769 1
< 0.1%
0.08105216833 1
< 0.1%
0.09230638511 1
< 0.1%
0.09498894395 1
< 0.1%
0.1029862708 1
< 0.1%
0.1030919908 1
< 0.1%
0.1031196191 1
< 0.1%
0.1056283097 1
< 0.1%
ValueCountFrequency (%)
0.9865928392 1
< 0.1%
0.9831047885 1
< 0.1%
0.979045035 1
< 0.1%
0.9757270951 1
< 0.1%
0.970871108 1
< 0.1%
0.9707200866 1
< 0.1%
0.967497107 1
< 0.1%
0.9673069716 1
< 0.1%
0.9650804258 1
< 0.1%
0.9648534111 1
< 0.1%

carmelo_prob2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3879
Distinct (%)99.2%
Missing63157
Missing (%)94.2%
Infinite0
Infinite (%)0.0%
Mean0.37245426
Minimum0.013407161
Maximum0.93952644
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.1 KiB
2025-04-05T20:45:08.865978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.013407161
5-th percentile0.096602817
Q10.21977237
median0.34687089
Q30.50436401
95-th percentile0.72410948
Maximum0.93952644
Range0.92611928
Interquartile range (IQR)0.28459164

Descriptive statistics

Standard deviation0.19296691
Coefficient of variation (CV)0.51809558
Kurtosis-0.52774075
Mean0.37245426
Median Absolute Deviation (MAD)0.14323585
Skewness0.44976919
Sum1456.6686
Variance0.037236228
MonotonicityNot monotonic
2025-04-05T20:45:08.955595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1826842484 3
 
< 0.1%
0.6485646703 3
 
< 0.1%
0.4720300497 3
 
< 0.1%
0.6441052941 3
 
< 0.1%
0.4700453626 3
 
< 0.1%
0.3719917614 3
 
< 0.1%
0.3937472281 3
 
< 0.1%
0.6072060314 3
 
< 0.1%
0.1518344321 3
 
< 0.1%
0.3627138293 3
 
< 0.1%
Other values (3869) 3881
 
5.8%
(Missing) 63157
94.2%
ValueCountFrequency (%)
0.01340716081 1
< 0.1%
0.01689521146 1
< 0.1%
0.020954965 1
< 0.1%
0.02427290492 1
< 0.1%
0.02912889196 1
< 0.1%
0.02927991339 1
< 0.1%
0.03250289305 1
< 0.1%
0.0326930284 1
< 0.1%
0.03491957424 1
< 0.1%
0.03514658886 1
< 0.1%
ValueCountFrequency (%)
0.9395264415 1
< 0.1%
0.9300131319 1
< 0.1%
0.9223630023 1
< 0.1%
0.9189478317 1
< 0.1%
0.9076936149 1
< 0.1%
0.9050110561 1
< 0.1%
0.8970137292 1
< 0.1%
0.8969080092 1
< 0.1%
0.8968803809 1
< 0.1%
0.8943716903 1
< 0.1%

score1
Real number (ℝ)

HIGH CORRELATION 

Distinct132
Distinct (%)0.2%
Missing56
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean104.69076
Minimum2
Maximum184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.1 KiB
2025-04-05T20:45:09.039646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile80
Q195
median105
Q3114
95-th percentile129
Maximum184
Range182
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.840576
Coefficient of variation (CV)0.14175631
Kurtosis0.34154229
Mean104.69076
Median Absolute Deviation (MAD)10
Skewness0.0025682762
Sum7015537
Variance220.24268
MonotonicityNot monotonic
2025-04-05T20:45:09.128313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102 1889
 
2.8%
106 1886
 
2.8%
103 1882
 
2.8%
105 1874
 
2.8%
107 1831
 
2.7%
104 1829
 
2.7%
99 1813
 
2.7%
101 1798
 
2.7%
108 1786
 
2.7%
109 1784
 
2.7%
Other values (122) 48640
72.5%
ValueCountFrequency (%)
2 1
 
< 0.1%
18 1
 
< 0.1%
33 1
 
< 0.1%
44 1
 
< 0.1%
45 1
 
< 0.1%
46 8
< 0.1%
47 3
 
< 0.1%
48 3
 
< 0.1%
49 7
< 0.1%
50 4
< 0.1%
ValueCountFrequency (%)
184 1
 
< 0.1%
177 1
 
< 0.1%
176 1
 
< 0.1%
173 2
< 0.1%
172 1
 
< 0.1%
171 1
 
< 0.1%
169 1
 
< 0.1%
168 1
 
< 0.1%
165 1
 
< 0.1%
163 3
< 0.1%

score2
Real number (ℝ)

HIGH CORRELATION 

Distinct124
Distinct (%)0.2%
Missing56
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean101.00475
Minimum0
Maximum186
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size524.1 KiB
2025-04-05T20:45:09.217286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile78
Q192
median101
Q3110
95-th percentile124
Maximum186
Range186
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.285121
Coefficient of variation (CV)0.1414302
Kurtosis0.3551202
Mean101.00475
Median Absolute Deviation (MAD)9
Skewness-0.036050428
Sum6768530
Variance204.06468
MonotonicityNot monotonic
2025-04-05T20:45:09.303582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 1969
 
2.9%
103 1952
 
2.9%
100 1949
 
2.9%
102 1939
 
2.9%
98 1915
 
2.9%
105 1891
 
2.8%
101 1886
 
2.8%
104 1882
 
2.8%
106 1863
 
2.8%
97 1843
 
2.7%
Other values (114) 47923
71.5%
ValueCountFrequency (%)
0 1
 
< 0.1%
19 1
 
< 0.1%
38 1
 
< 0.1%
40 2
 
< 0.1%
43 2
 
< 0.1%
44 2
 
< 0.1%
46 6
< 0.1%
47 5
< 0.1%
48 8
< 0.1%
49 7
< 0.1%
ValueCountFrequency (%)
186 1
 
< 0.1%
169 1
 
< 0.1%
166 2
 
< 0.1%
162 3
< 0.1%
161 1
 
< 0.1%
160 1
 
< 0.1%
157 1
 
< 0.1%
156 7
< 0.1%
155 4
< 0.1%
154 5
< 0.1%

Interactions

2025-04-05T20:44:57.063207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:36.188450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:37.747416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:39.172735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:40.546807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:41.966356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:43.329906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:44.696706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:46.144056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:47.590321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:48.950013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:50.307738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:51.654228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:53.015199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:54.357735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:55.625015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:57.161815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:36.318881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:37.841237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:39.262198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:40.639935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:42.054409image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:43.417321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:44.790289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:46.236469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:47.684085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:49.038760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:50.400351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:51.748519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:53.100207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:54.443487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:55.717064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:57.250247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:36.411651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:37.929078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:39.354668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:40.729542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:42.139422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:43.528029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:44.886866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:46.333650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:47.772229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:49.129890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:50.489324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:51.834991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:53.186763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:54.526630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:55.830110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:57.362190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:36.511335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:38.026145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:39.443667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:40.821810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:42.228552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:43.620685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:44.980417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:46.428670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:47.850932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:49.213708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:50.570830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:51.922424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:53.270682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:54.602060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:55.927974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:57.454531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:36.614442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:38.122663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:39.538220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:40.914160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:42.314989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:43.712351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:45.072215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:46.527680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:47.933726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:49.296752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:50.657131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:52.002356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:53.372040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:54.681897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:56.026539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:57.538810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:36.703402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:38.209601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:39.621918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:40.999062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:42.396075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:43.791794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:45.156998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:46.629409image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:48.008307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:49.374103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:50.734135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:52.092791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:53.448836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:54.753035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:56.114050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:45:02.952839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:36.786272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:38.295293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:39.707738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:41.081747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:42.471953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:43.869681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:45.241372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:46.716481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:48.082120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:49.450353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:50.814933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:52.173021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:53.523853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:54.827217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:56.202460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:45:03.066642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:36.879859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:38.389129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:39.799369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:41.175592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:42.559241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:43.957427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:45.331374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:46.810650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:48.163011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:49.531071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:50.895413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:52.255754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:53.605535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:54.904126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:56.298984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:45:03.158230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:36.966558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:38.476731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:39.883139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:41.260855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:42.644935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:44.038913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:45.420119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:46.899611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:48.247144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:49.611805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:50.979798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:52.335826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:53.685220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:54.978767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:56.386242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:45:03.241668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:37.053688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:38.564335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:39.964685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:41.342902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:42.723150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:44.115783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:45.502318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:46.984678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:48.328756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:49.700787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:51.064049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:52.424770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:53.765219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:55.063778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:56.467025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:45:03.324729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:37.138913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:38.649394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:40.046191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:41.423604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:42.802764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:44.193151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:45.587331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:47.089967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:48.415781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:49.783550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:51.149049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:52.506361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:53.854792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:55.141825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:56.549752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:45:03.407063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:37.225901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:38.737158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:40.129487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:41.502204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:42.881991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:44.270513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:45.703972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:47.170855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:48.497823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:49.873439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:51.232934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:52.611869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:53.936536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:55.226363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:56.639731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:45:03.501534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:37.317651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:38.820210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:40.206775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:41.602340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:42.962235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:44.353572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:45.789717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:47.252949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:48.592320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:49.958304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:51.319592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:52.697402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:54.028610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:55.306324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:56.717984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:45:03.583614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:37.402149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:38.901049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:40.281284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:41.677525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:43.032384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:44.435218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:45.869356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:47.327907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:48.701705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:50.044571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:51.401246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:52.775196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:54.134819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:55.379614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:56.791633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:45:03.672317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:37.486390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:38.993407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:40.363464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:41.780175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:43.113656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:44.516089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:45.953092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:47.409848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:48.783802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:50.137266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:51.483336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:52.851177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:54.203708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:55.454420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:56.876775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:45:03.767174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:37.582192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:39.086124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:40.457476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:41.876891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:43.240180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:44.613377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:46.042440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:47.510167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:48.857593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:50.219202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:51.566314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:52.928250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:54.276527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:55.528540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2025-04-05T20:44:56.966829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2025-04-05T20:45:09.395323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
carmelo1_postcarmelo1_precarmelo2_postcarmelo2_precarmelo_prob1carmelo_prob2df_indexelo1_postelo1_preelo2_postelo2_preelo_prob1elo_prob2neutralplayoffscore1score2season
carmelo1_post1.0000.9950.0100.0190.671-0.6710.0130.9730.9660.0100.0210.660-0.6600.0210.3320.231-0.191-0.020
carmelo1_pre0.9951.0000.0160.0240.674-0.6740.0350.9670.9720.0170.0260.663-0.6630.0000.3250.190-0.147-0.002
carmelo2_post0.0100.0161.0000.995-0.6610.6610.0140.0090.0180.9710.964-0.6490.6490.0000.362-0.1750.197-0.017
carmelo2_pre0.0190.0240.9951.000-0.6620.6620.0310.0180.0250.9650.970-0.6490.6490.0000.367-0.1320.153-0.006
carmelo_prob10.6710.674-0.661-0.6621.000-1.000-0.0010.6550.655-0.644-0.6410.957-0.9570.0870.0000.242-0.215-0.001
carmelo_prob2-0.671-0.6740.6610.662-1.0001.0000.001-0.655-0.6550.6440.641-0.9570.9570.0870.000-0.2420.2150.001
df_index0.0130.0350.0140.031-0.0010.0011.0000.1210.1260.1140.1120.011-0.0110.0220.327-0.192-0.1781.000
elo1_post0.9730.9670.0090.0180.655-0.6550.1211.0000.9960.0810.0890.656-0.6560.0000.1510.134-0.1860.120
elo1_pre0.9660.9720.0180.0250.655-0.6550.1260.9961.0000.0870.0880.661-0.6610.0000.1620.101-0.1570.124
elo2_post0.0100.0170.9710.965-0.6440.6440.1140.0810.0871.0000.996-0.6520.6520.0000.116-0.2000.0890.113
elo2_pre0.0210.0260.9640.970-0.6410.6410.1120.0890.0880.9961.000-0.6540.6540.0000.128-0.1660.0590.110
elo_prob10.6600.663-0.649-0.6490.957-0.9570.0110.6560.661-0.652-0.6541.000-1.0000.0220.0430.197-0.1600.011
elo_prob2-0.660-0.6630.6490.649-0.9570.957-0.011-0.656-0.6610.6520.654-1.0001.0000.0220.043-0.1970.160-0.011
neutral0.0210.0000.0000.0000.0870.0870.0220.0000.0000.0000.0000.0220.0221.0001.000-0.0040.0090.019
playoff0.3320.3250.3620.3670.0000.0000.3270.1510.1620.1160.1280.0430.0431.0001.000-0.037-0.027-0.384
score10.2310.190-0.175-0.1320.242-0.242-0.1920.1340.101-0.200-0.1660.197-0.197-0.004-0.0371.0000.580-0.193
score2-0.191-0.1470.1970.153-0.2150.215-0.178-0.186-0.1570.0890.059-0.1600.1600.009-0.0270.5801.000-0.178
season-0.020-0.002-0.017-0.006-0.0010.0011.0000.1200.1240.1130.1100.011-0.0110.019-0.384-0.193-0.1781.000

Missing values

2025-04-05T20:45:03.931471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-05T20:45:04.348836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-05T20:45:04.846421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

df_indexdateseasonneutralplayoffteam1team2elo1_preelo2_preelo_prob1elo_prob2elo1_postelo2_postcarmelo1_precarmelo2_precarmelo1_postcarmelo2_postcarmelo_prob1carmelo_prob2score1score2
001946-11-0119470NaNTRHNYK1300.00001300.00000.6400650.3599351293.27671306.7233NaNNaNNaNNaNNaNNaN66.068.0
111946-11-0219470NaNCHSNYK1300.00001306.72330.6311010.3688991309.65211297.0712NaNNaNNaNNaNNaNNaN63.047.0
221946-11-0219470NaNPROBOS1300.00001300.00000.6400650.3599351305.15421294.8458NaNNaNNaNNaNNaNNaN59.053.0
331946-11-0219470NaNSTBPIT1300.00001300.00000.6400650.3599351304.69081295.3092NaNNaNNaNNaNNaNNaN56.051.0
441946-11-0219470NaNDTFWSC1300.00001300.00000.6400650.3599351279.61891320.3811NaNNaNNaNNaNNaNNaN33.050.0
551946-11-0319470NaNCLRTRH1300.00001293.27670.6489320.3510681307.12331286.1534NaNNaNNaNNaNNaNNaN71.060.0
661946-11-0419470NaNPITWSC1295.30921320.38110.6061890.3938111277.94561337.7448NaNNaNNaNNaNNaNNaN56.071.0
771946-11-0519470NaNBOSCHS1294.84581309.65210.6202040.3797961288.41391316.0840NaNNaNNaNNaNNaNNaN55.057.0
881946-11-0519470NaNDTFSTB1279.61891304.69080.6061890.3938111271.46241312.8473NaNNaNNaNNaNNaNNaN49.053.0
991946-11-0719470NaNPHWPIT1300.00001277.94560.6687640.3312361304.66701273.2786NaNNaNNaNNaNNaNNaN81.075.0
df_indexdateseasonneutralplayoffteam1team2elo1_preelo2_preelo_prob1elo_prob2elo1_postelo2_postcarmelo1_precarmelo2_precarmelo1_postcarmelo2_postcarmelo_prob1carmelo_prob2score1score2
67058670582018-04-2620180qMINHOU1551.4216141732.2137410.3857820.614218NaNNaN1564.6206201748.389768NaNNaN0.3514350.648565NaNNaN
67059670592018-04-2620180qWASTOR1482.6711741675.6162150.3693430.630657NaNNaN1487.3226241677.021761NaNNaN0.3558950.644105NaNNaN
67060670602018-04-2720180qGSWSAS1577.9648141580.1714330.6371330.362867NaNNaN1585.1269251581.581732NaNNaN0.6394880.360512NaNNaN
67061670612018-04-2720180qBOSMIL1575.4807251514.0494440.7169280.283072NaNNaN1578.3178761507.976699NaNNaN0.7272540.272746NaNNaN
67062670622018-04-2720180qPORNOP1614.2630411588.2903730.6737420.326258NaNNaN1618.4011111599.536602NaNNaN0.6535830.346417NaNNaN
67063670632018-04-2720180qPHIMIA1660.4103981505.7367670.8124520.187548NaNNaN1670.6845301500.681024NaNNaN0.8173160.182684NaNNaN
67064670642018-04-2820180qHOUMIN1732.2137411551.4216140.8342940.165706NaNNaN1748.3897681564.620620NaNNaN0.8481660.151834NaNNaN
67065670652018-04-2820180qOKCUTA1619.5593641686.3391860.5476630.452337NaNNaN1632.2770461677.637894NaNNaN0.6372860.362714NaNNaN
67066670662018-04-2820180qCLEIND1552.2527171556.7218430.6341170.365883NaNNaN1551.3058681551.412437NaNNaN0.7335810.266419NaNNaN
67067670672018-04-2820180qTORWAS1675.6162151482.6711740.8437410.156259NaNNaN1677.0217611487.322624NaNNaN0.8416450.158355NaNNaN